The present invention relates to a method of detecting differences in breast tissue in subsequent images of the same breast.
Breast cancer is one of the largest serious diseases among women in the western world. It is the most common cancer in women accounting for nearly one out of every three cancers diagnosed in the United States. It is also the most common and deadly cancer for women on a global scale, where breast cancer accounts for 21% of all cancer cases and 14% of all cancer deaths.
However, if detected sufficiently early, there is a high probability of survival. Detection of breast cancer can be very difficult, since the first signs of breast cancer are often asymptomatic.
Mammograms have thus far been found to be the most effective way to detect breast cancer early, sometimes up to two years before a lump in the breast can be felt. Mammography is a specific type of imaging that uses a low-dose x-ray system. Once an image has been developed, doctors examine the image to look for signs that cancer is developing. Naturally, where human intervention is required, there is room for error or misjudgement. A lot of effort has therefore been put into the field of improving the processing of mammograms. The mammograms are mainly analysed by radiologists who look for abnormalities that might indicate breast cancer. These abnormalities include small calcifications, masses and focal asymmetries.
Various breast imaging techniques now exist that attempt to detect breast cancer earlier and in a more predictable fashion.
Digital mammography, also called full-field digital mammography (FFDM), is a mammography system in which the x-ray film is replaced by solid-state detectors that convert x-rays into electrical signals. These detectors are similar to those found in digital cameras. The electrical signals are used to produce images of the breast that can be seen on a computer screen or printed on special film similar to conventional mammograms. From the patient's point of view, digital mammography is essentially the same as the screen-film system.
Computer-aided detection (CAD) systems use a digitised mammographic image that can be obtained from either a conventional film mammogram or a digitally acquired mammogram. The computer software then searches for abnormal areas of density or calcification that may indicate the presence of cancer. The CAD system highlights these areas on the images, alerting the radiologist to the need for further analysis.
The present invention uses change in breast tissue to identify the possible risk of cancer. The methods of the invention described below do not seek to locate features within the image used, but rather assign an overall score to the image which is indicative of the probability of the image being associated with a higher breast density and hence providing a measure of the risk of cancer.
Several approaches to automatic or semi-automatic assessment of mammographic breast density have been suggested previously. The majority of these have been aimed at reproducing the radiologists' categorical rating system. Boone et al [Journal of Digital Imaging 11(3) August 1998, 101-115] aimed at making a continuously scaled breast density index. Six mathematical features were calculated from the mammograms and used in conjunction with single value decomposition and multiple linear regression to calculate a computerised breast density. The training was done using a collection of mammograms sorted by their density as perceived by an expert.
Karssemeijer [Physics in Medicine and Biology 43 (1998) 365-378] divided the breast area into different regions and extracted features based on the grey level histograms of these regions. Using these features a kNN classifier is trained to classify a mammogram into one of four density categories. Byng et al [Physics in Medicine and Biology 41 (1996) 909-923] used measures of the skewness of the grey level histogram and of image texture characterised by the fractal dimension. They showed that both measures are correlated with the radiologists' classifications of the mammographic density. Tromans et al and Petroudi et al [in Astley et al; International workshop on Digital Mammography, Springer 2006, 26-33 and 609-615] used automated density assessment employing both physics based modelling and texture based learning of BI-RADS categories and Wolfe Patterns.
The Breast Imaging Reporting and Data System (BI-RADS) is a four category scheme proposed by the American College of Radiology. The BI-RADS categories are:    1. Entirely fatty    2. Fatty with scattered fibroglandular tissue    3. Heterogeneously dense    4. Extremely dense.
In practice, these classifications are used to alert clinicians that the ability to detect small cancers in the dense breast is reduced. The four categories are represented by the numbers one to four in order of increasing density.
Others, including Zhou et al [Medical physics 28(6), June 2001, 1056-1069] have used thresholding of the image based on properties of the grey level histogram to get an estimate of the percentage of density in the breast or [Yaffe et al 1994] use thresholding done by a radiologist. In the thresholding method, the reading radiologist determines an intensity threshold using a slider in a graphical user interface. The radiologist is assisted visually by a display showing the amount of dense tissue corresponding to the current slider position. The density is defined as the ratio between segmented dense tissue and total area of breast tissue. The continuous nature of such threshold adjustment makes the method more sensitive than the BI-RADS, Wolfe patterns and related scoring systems with a low number of categories when detecting or monitoring, perhaps small, density changes.
A currently frequently discussed issue related to breast cancer risk is the potential influence of hormone replacement therapy taken after the menopause. If breast density is indeed a surrogate measure of risk for developing cancer in the breast, a sensitive measure of changes in breast density during hormone dosing provides an estimate of the gynaecological safety of a given treatment modality. Hence, the concept of breast density has an ongoing interest.
The meaning of the word density depends on the context. The physical density states how much the breast tissue attenuates x-rays locally. An assessment of the projected area and specifically the distribution of fibroglandular tissue is often called dense tissue, and can be thought of as a “biological density”. This can be considered as an intrinsic property of the entire breast, and is the type of density referred to in the context of Wolfe Patters and related assessments.
A further aspect of the invention described below relates to an improved coordinate system for registration of images.
Numerous studies have investigated the relation between mammographic density and breast cancer risk, and women with high breast density appear to have a four to six fold increase in breast cancer risk [Wolfe 1976, van Gils 1999, Boyd 2005]. More recently, it has been suggested that heterogeneity of mammogram texture is also related with mammographic risk [Raundahl 2006, Karemore 2009]. However, the link between heterogeneity of mammogram and breast density with breast cancer risk is not yet well established.
Our previous work [Raundahl 2008] suggests the framework for obtaining accurate and sensitive measurements of breast density change due to various hormonal replacement therapies by calculating texture change (Heterogeneity examination of mammograms). These methods include calculation of N-JET features [Koenderink 1987] (Gaussian derivatives up to order of three in four different scale) considering image coordinates as a orthonormal base vectors, typically aligned with the rows and columns of the image. There are disadvantages with expressing coordinates in relation to mammogram image frame and not with the anatomy of the breast tissue structures.
The breast is a mass of glandular, fatty, and fibrous tissues positioned over the pectoral muscles of the chest wall and attached to the chest wall by fibrous strands called Cooper's ligaments. A layer of fatty tissue surrounds the breast glands and extends throughout the breast. The fatty tissue gives the breast a soft consistency. The glandular tissues of the breast house the lobules (milk producing glands at the ends of the lobes) and the ducts (milk passages). Toward the nipple, each duct widens to form a sac (ampulla). These structures motivate us to derive a coordinate system for the breast in a mammogram considering anatomical orientation of tissues.
To perform automated analysis of 2D mammograms, most of the previous approaches first segment the breast region and then do the analysis inside the breast region in the image. It is common to analyse the images in the x-y coordinate system and even discard the position information of the extracted features. However, the x and y-axis directions do not have a direct anatomical meaning and due to the variability between the shapes of the breasts, a fixed direction in one mammogram may anatomically correspond to a completely different direction in another.
In this work, we have developed an anatomical breast coordinate system that identifies corresponding positions and locations between any arbitrary two mediolateral (ML) or mediolateral oblique (MLO) view mammograms. Our starting hypothesis was that feature registration according to anatomical orientations and positions gives additional discrimination power to feature classification between cancer and control patient groups. Since there is a huge variability between breasts, it is problematic to obtain direct position and orientation correspondence between an arbitrary two female breasts.
Our work is related to previous work that has considered registration of mammograms, either as bilateral registration, or longitudinal registration (see e.g. Raundahl 2008). The breast coordinate transform described here can be also seen as a registration method, but due to its construction there is no need to explicitly warp the images. The idea instead is to identify the anatomical coordinate system for the images and extract the features with respect to that coordinate frame, i.e. in the orientations and positions defined by the breast coordinate frame but maintaining the scale of the image. In other words, we define the mapping that defines the local correspondence between any two breasts as soon as their anatomical landmarks or breast parameters have been identified.
There are several works in the literature addressing the automatic extraction of the anatomical landmarks. The localization of the pectoral muscle has been considered e.g. in [Karssemeijer 1998, Kwok 2004, Kinoshita 2008], the nipple location in [Yin 1994, Mendez 1996, Chandrasekhar 1997, Zhou 2004, Karnan 2007, Kinoshita 2008], and breast boundary in [Yin 1994, Bick 1995, Mendez 1996, Ojala 2001, Karnan 2007]. There is therefore no need to describe here the automatic finding of these features, and the starting point for our work is that the line approximating the pectoral muscle, the nipple location, and breast boundary approximation are known or obtained manually.
Georgsson 2003 considered bilateral registration for bilateral comparison; in addition, there are several works that address the registration of breasts in three dimensions [Kita 2002, Yam 2001, Yang 2005]. Georgsson defined the two dimensional coordinate system by the distance from the skin-line and the distance to the nipple-line along to an equidistant curve (equidistant from the skin line). We instead describe below construction of a nonlinear parametric coordinate system based on a subset of second order curves. Our coordinate system is minimally identified from the location of the nipple, two points on the breast boundary, boundary normal direction at the nipple, and the pectoral line. The method is generalisable to other kinds of image.